{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Statistics for Time Series Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries and dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('shampoo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Show dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1-01</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1-04</td>\n",
       "      <td>119.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1-05</td>\n",
       "      <td>180.3</td>\n",
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       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1-06</td>\n",
       "      <td>168.5</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1-07</td>\n",
       "      <td>231.8</td>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1-08</td>\n",
       "      <td>224.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1-09</td>\n",
       "      <td>192.8</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1-10</td>\n",
       "      <td>122.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1-11</td>\n",
       "      <td>336.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1-12</td>\n",
       "      <td>185.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2-01</td>\n",
       "      <td>194.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2-02</td>\n",
       "      <td>149.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2-03</td>\n",
       "      <td>210.1</td>\n",
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       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2-04</td>\n",
       "      <td>273.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2-05</td>\n",
       "      <td>191.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2-06</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2-07</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2-08</td>\n",
       "      <td>303.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>2-09</td>\n",
       "      <td>289.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>2-10</td>\n",
       "      <td>421.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>2-11</td>\n",
       "      <td>264.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>2-12</td>\n",
       "      <td>342.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>3-01</td>\n",
       "      <td>339.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>3-02</td>\n",
       "      <td>440.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>3-03</td>\n",
       "      <td>315.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>3-04</td>\n",
       "      <td>439.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>3-05</td>\n",
       "      <td>401.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>3-06</td>\n",
       "      <td>437.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>3-07</td>\n",
       "      <td>575.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>3-08</td>\n",
       "      <td>407.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>3-09</td>\n",
       "      <td>682.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>3-10</td>\n",
       "      <td>475.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>3-11</td>\n",
       "      <td>581.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>3-12</td>\n",
       "      <td>646.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month  Sales\n",
       "0   1-01  266.0\n",
       "1   1-02  145.9\n",
       "2   1-03  183.1\n",
       "3   1-04  119.3\n",
       "4   1-05  180.3\n",
       "5   1-06  168.5\n",
       "6   1-07  231.8\n",
       "7   1-08  224.5\n",
       "8   1-09  192.8\n",
       "9   1-10  122.9\n",
       "10  1-11  336.5\n",
       "11  1-12  185.9\n",
       "12  2-01  194.3\n",
       "13  2-02  149.5\n",
       "14  2-03  210.1\n",
       "15  2-04  273.3\n",
       "16  2-05  191.4\n",
       "17  2-06  287.0\n",
       "18  2-07  226.0\n",
       "19  2-08  303.6\n",
       "20  2-09  289.9\n",
       "21  2-10  421.6\n",
       "22  2-11  264.5\n",
       "23  2-12  342.3\n",
       "24  3-01  339.7\n",
       "25  3-02  440.4\n",
       "26  3-03  315.9\n",
       "27  3-04  439.3\n",
       "28  3-05  401.3\n",
       "29  3-06  437.4\n",
       "30  3-07  575.5\n",
       "31  3-08  407.6\n",
       "32  3-09  682.0\n",
       "33  3-10  475.3\n",
       "34  3-11  581.3\n",
       "35  3-12  646.9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>145.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1-03</td>\n",
       "      <td>183.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1-04</td>\n",
       "      <td>119.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1-05</td>\n",
       "      <td>180.3</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  Month  Sales\n",
       "0  1-01  266.0\n",
       "1  1-02  145.9\n",
       "2  1-03  183.1\n",
       "3  1-04  119.3\n",
       "4  1-05  180.3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1-01</td>\n",
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       "      <td>1-02</td>\n",
       "      <td>145.9</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1-03</td>\n",
       "      <td>183.1</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1-04</td>\n",
       "      <td>119.3</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1-05</td>\n",
       "      <td>180.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1-06</td>\n",
       "      <td>168.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1-07</td>\n",
       "      <td>231.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1-08</td>\n",
       "      <td>224.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1-09</td>\n",
       "      <td>192.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1-10</td>\n",
       "      <td>122.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month  Sales\n",
       "0  1-01  266.0\n",
       "1  1-02  145.9\n",
       "2  1-03  183.1\n",
       "3  1-04  119.3\n",
       "4  1-05  180.3\n",
       "5  1-06  168.5\n",
       "6  1-07  231.8\n",
       "7  1-08  224.5\n",
       "8  1-09  192.8\n",
       "9  1-10  122.9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset summary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>312.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>148.937164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>119.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>192.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>280.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>411.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>682.000000</td>\n",
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      ],
      "text/plain": [
       "            Sales\n",
       "count   36.000000\n",
       "mean   312.600000\n",
       "std    148.937164\n",
       "min    119.300000\n",
       "25%    192.450000\n",
       "50%    280.150000\n",
       "75%    411.100000\n",
       "max    682.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales    312.6\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales    148.937164\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001C1ABAE2388>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 7.5]); # Set dimensions for figure\n",
    "\n",
    "plt.hist(data['Sales'])\n",
    "plt.title('Histogram of Shampoo Sales');\n",
    "plt.xlabel('Shampoo Sales ($M)');\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TnF5Vr62q45ZaGQDANrXhe7C6+1NJnp3kmUl+OMkfVtUnquonllUcAMB2tNF7sO5eVc9Pcm6SByd5RHd///z++UusDwBg2zlwg/u9KMnLkvxad399ZWV3f66qnr2UygAAtqmNBqyHJfl6d1+dJFV1gyQ36e7Lu/tVS6sOAGAb2ug9WH+T5KYLywfN6wAAWGWjAesm3X3ZysL8/qDllAQAsL1tNGD9c1Uds7JQVfdO8vV19gcA+K610XuwnpbkTVX1uXn51kl+ajklAQBsbxsKWN39oaq6c5Kjk1SST3T3N5ZaGQDANrXREawkuU+SXfMx96qqdPcrl1IVAMA2tqGAVVWvSnKHJGcmuXpe3UkELACAVTY6grU7yV26u5dZDADA/mCjnyI8O8m/WmYhAAD7i42OYB2R5ONV9cEkV6ys7O5HLqUqAIBtbKMB68RlFgEAsD/Z6Nc0/F1V3T7Jnbr7b6rqoCQHLLc0AIDtaUP3YFXVE5OcnOQl86rbJnnbsooCANjONnqT+5OTHJvkkiTp7k8lueWyigIA2M42GrCu6O4rVxaq6sBM34MFAMAqGw1Yf1dVv5bkplV1XJI3Jfmz5ZUFALB9bTRgPSvJF5OcleQ/JfmLJM9eVlEAANvZRj9F+M0kL5tfAACsY6PPIvxM1rjnqruPGl4RAMA2d22eRbjiJkn+fZLDx5cDALD9begerO7+8sLrs939giQPXnJtAADb0kanCI9ZWLxBphGtQ5ZSEQDANrfRKcI/WHh/VZLzk/zk8GoAAPYDG/0U4YOWXQgAwP5io1OEv7ze9u5+3hrH3CTJaUluPLdzcnf/1nUpEgBgO7k2nyK8T5K3z8uPyBSeLljnmCuSPLi7L6uqGyZ5T1X9ZXe//zpXCwCwDWw0YB2R5JjuvjRJqurEJG/q7v+4pwO6u5NcNi/ecH55fiEAsN/b6KNydia5cmH5yiS79nZQVR1QVWcmuSjJKd39gWtdIQDANrPREaxXJflgVb010yjUo5K8cm8HdffVSe5ZVYcleWtV3bW7z17cp6pOSHJCkuzcufPa1H6dXH3KRi/5ujvguKuW3gZb02b8fm2W/en3eH/5d9mf/k1gf7fRLxr93SSPT/LVJBcneXx3P3ejjXT3xUlOTfLQNba9tLt3d/fuHTt2bPSUAABb1kanCJPkoCSXdPcLk1xYVd+73s5VtWMeuUpV3TTJQ5J84jpXCgCwTWz0axp+K9MnCY9O8opMN6y/Osmx6xx26yR/WlUHZApyb+zud1y/cgEAtr6N3pjwqCT3SvLhJOnuz1XVuo/K6e6PzccAAHxX2egU4ZXz1y50klTVzZZXEgDA9rbRgPXGqnpJksOq6olJ/ibJy5ZXFgDA9rXRZxH+flUdl+SSTPdh/WZ3n7LUygAAtqm9Bqz5JvW/6u6HJBGqAAD2Yq9ThPOXhV5eVYduQj0AANveRj9F+C9JzqqqU5L888rK7v7FpVQFALCNbTRg/fn8AgBgL9YNWFW1s7v/b3f/6WYVBACw3e3tHqy3rbypqjcvuRYAgP3C3gJWLbw/apmFAADsL/YWsHoP7wEA2IO93eR+j6q6JNNI1k3n95mXu7tvvtTqAAC2oXUDVncfsFmFAADsLzb6LEIAADZIwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYTMACABhMwAIAGEzAAgAYbGkBq6qOrKq/rapzq+qcqnrqstoCANhKDlziua9K8vTu/nBVHZLkjKo6pbs/vsQ2AQD2uaWNYHX357v7w/P7S5Ocm+S2y2oPAGCr2JR7sKpqV5J7JfnAZrQHALAvLXOKMElSVQcneXOSp3X3JWtsPyHJCUmyc+fOZZezKa4+ZendCsAWsRl/8w847qqlt8FYSx3BqqobZgpXr+nut6y1T3e/tLt3d/fuHTt2LLMcAIBNscxPEVaSP0lybnc/b1ntAABsNcscwTo2yc8meXBVnTm/HrbE9gAAtoSlTRx393uS1LLODwCwVfkmdwCAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBlhawqurlVXVRVZ29rDYAALaiZY5gnZTkoUs8PwDAlrS0gNXdpyX5yrLODwCwVbkHCwBgsAP3dQFVdUKSE5Jk586d+7gagK3r6lM250/2AcddtSntsLVs1u/XZtgKv8P7fASru1/a3bu7e/eOHTv2dTkAANfbPg9YAAD7m2V+TcPrkrwvydFVdWFVPWFZbQEAbCVLm3Dt7scu69wAAFuZKUIAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwQQsAIDBBCwAgMEELACAwZYasKrqoVX1yao6r6qetcy2AAC2iqUFrKo6IMkfJfm3Se6S5LFVdZdltQcAsFUscwTrvknO6+5Pd/eVSV6f5MeX2B4AwJawzIB12yQXLCxfOK8DANivHbjEc9ca6/oaO1WdkOSEefGyqvrkOuc8IsmXBtTG+ja5n9f6VfmusMV/n/ebf5ct3s9b0XX6t9fPS/WtfxP9vCHX++/XRvv59nvasMyAdWGSIxeWb5fkc6t36u6XJnnpRk5YVad39+4x5bEn+nlz6OfNoZ83h37eHPp5c4zo52VOEX4oyZ2q6nur6kZJHpPk7UtsDwBgS1jaCFZ3X1VVT0nyV0kOSPLy7j5nWe0BAGwVy5wiTHf/RZK/GHjKDU0lcr3p582hnzeHft4c+nlz6OfNcb37ubqvcd85AADXg0flAAAMtmUCVlW9vKouqqqzF9YdXlWnVNWn5p/fM6+vqvrD+RE8H6uqY/Zd5dtLVR1ZVX9bVedW1TlV9dR5vb4eqKpuUlUfrKqPzv382/P6762qD8z9/Ib5AyCpqhvPy+fN23fty/q3m6o6oKo+UlXvmJf18xJU1flVdVZVnVlVp8/r/O0YrKoOq6qTq+oT89/q++vnsarq6Pn3eOV1SVU9bWQ/b5mAleSkJA9dte5ZSd7V3XdK8q55OZkev3On+XVCkhdvUo37g6uSPL27vz/J/ZI8uaZHGOnrsa5I8uDuvkeSeyZ5aFXdL8nvJXn+3M9fTfKEef8nJPlqd98xyfPn/di4pyY5d2FZPy/Pg7r7ngsfYfe3Y7wXJnlnd985yT0y/W7r54G6+5Pz7/E9k9w7yeVJ3pqR/dzdW+aVZFeSsxeWP5nk1vP7Wyf55Pz+JUkeu9Z+Xte6z/9PkuP09VL7+KAkH07yg5m+uO7Aef39k/zV/P6vktx/fn/gvF/t69q3wyvTd+y9K8mDk7wj0zcM6ufl9PX5SY5Ytc7fjrF9fPMkn1n9e6mfl9rnP5bk70f381YawVrLrbr780ky/7zlvN5jeAaYp0fuleQD0dfDzdNWZya5KMkpSf4hycXdfdW8y2Jffquf5+1fS3KLza1423pBkmck+ea8fIvo52XpJH9dVWfU9BSOxN+O0Y5K8sUkr5invf+4qm4W/bxMj0nyuvn9sH7e6gFrTzb0GB72rKoOTvLmJE/r7kvW23WNdfp6A7r76p6Gn2+X6eHn37/WbvNP/XwdVNXDk1zU3Wcsrl5jV/08xrHdfUym6ZInV9UD19lXX183ByY5JsmLu/teSf45356mWot+vh7m+zMfmeRNe9t1jXXr9vNWD1hfqKpbJ8n886J5/YYew8PaquqGmcLVa7r7LfNqfb0k3X1xklMz3fN2WFWtfP/cYl9+q5/n7Ycm+crmVrotHZvkkVV1fpLXZ5omfEH081J09+fmnxdlul/lvvG3Y7QLk1zY3R+Yl0/OFLj083L82yQf7u4vzMvD+nmrB6y3J/n5+f3PZ7pfaGX9z8139d8vyddWhvRYX1VVkj9Jcm53P29hk74eqKp2VNVh8/ubJnlIphtV/zbJo+fdVvfzSv8/Osm7e57oZ8+6+1e7+3bdvSvTMP+7u/tx0c/DVdXNquqQlfeZ7ls5O/52DNXd/5Tkgqo6el71o0k+Hv28LI/Nt6cHk5H9vK9vLlu4Yex1ST6f5BuZkuITMt0b8a4kn5p/Hj7vW0n+KNM9LWcl2b2v698uryQPyDSs+bEkZ86vh+nr4f189yQfmfv57CS/Oa8/KskHk5yXaUj6xvP6m8zL583bj9rX17DdXkl+JMk79PPS+veoJB+dX+ck+fV5vb8d4/v6nklOn/9+vC3J9+jnpfTzQUm+nOTQhXXD+tk3uQMADLbVpwgBALYdAQsAYDABCwBgMAELAGAwAQsAYDABC1hTVf16VZ0zPzn+zKr6wXn9+VV1xL6ubyOq6qCqek1VnVVVZ1fVe+anGKx3zJDrq6qnVdXPrVp34qrlXVXVVfWchXVHVNU3qupF8/JTqurx17ceYHMJWMA1VNX9kzw8yTHdffdMX5R6wfpHbUlPTfKF7r5bd9810/frfWPZjc7fEv8fkrx2Xr5LVZ2W5L9U1Yer6rELu386U1+v+PeZvmdqxcuT/OKSSwYGE7CAtdw6yZe6+4ok6e4v9fyYlNkvzEHhrKq6c5JU1X2r6r3zA2rfu/JN1FV1fFW9rar+rKo+M4/I/PK83/ur6vB5v1Or6gXzsWdX1X3n9YfPx39s3v/u661f4zo+u7LQ3Z9cuab52DPmUboT1jg2VfUzVfXBeQTvJTU9wPuAqjpprvGsqvqlNQ59cKbHb6w8cPrEJK9M8uJMj/f50MK+X09yblXtnpd/KskbF2q+PMn5K/0BbA8CFrCWv05yZFX9f1X1v6rqh1dt/1JPD/19cZJfmdd9IskDe3pA7W8mee7C/ndN8tOZnl33u0kun/d7X5LFabSbdfcPJXlSppGbJPntJB+ZR9J+LVNQWW/9opcneWZVva+qfqeq7rSw7T90972T7E7yi1V1i8UDq+r7M4WdY3t6aPfVSR6X6Vu2b9vdd+3uuyV5xRrtHptk8QHUVya5ZZIbdPfXu/u8Vfu/Psljqup2czurn3F2epJ/vUY7wBYlYAHX0N2XJbl3khOSfDHJG6rq+IVdVh4SfkaSXfP7Q5O8qarOTvL8JD+wsP/fdvel3f3FJF9L8mfz+rMWjk/mZ4J192lJbj4/z/EBSV41r393kltU1aHrrF+8jjMzPeLl/01yeJIPzcEpmULVR5O8P9NDXBfDVzI9A+7e8zFnzstHZZrSO6qq/mdVPTTJJWt04a3nflvxzCR3S/KUeSTvHqv2f2eS4zI9F+0Na5zvoiS3WWM9sEUduPddgO9G3X11klOTnFpVZ2V68OlJ8+Yr5p9X59t/R56TKUg9qqp2zcdm1f5J8s2F5W/mO/8OrTMzXoUAAAH5SURBVH52V2d6Btg1yltn/erruCxTIHxLVX0zycOq6laZ7iu7f3dfXlWnZnpO4aJK8qfd/aurzzkHpH+T5MlJfjLT/VaLvr54vu7+bJLHVtV/zTQ9+JYkd1jYfmVVnZHk6ZmC6SNWne8m8zmBbcIIFnANVXX0qum0eyb5x70cdmi+fb/T8dex6Z+a239ApqfVfy3JaZmm5lJVP5JpevKSddYvXsexVfU98/sbJbnLfB2HJvnqHK7unOR+a9TyriSPrqpbzscfXlW3nz9heIPufnOS30hyzBrHnpvkjgt1rIzmfTPTqN/N1jjmD5I8s7u/vMa278v00HBgmzCCBazl4CT/c56iuyrJeZmmC9fzP5L8aVX9cpJ3X8d2v1pV701y83x7VOjEJK+oqo8luTzTSNp66xfdIcmLq6oy/R/KP0/y5iQ3SvKf52M/mWma8Dt098er6tlJ/rqqbpDp04dPzjSS9Ip5XZJcY4QryV9mnr6c/URV/Ummab5HZ41PBXb3OfnOTw8uOjbTPWfANlHd1xhRB9h08zTdr3T36fu6lhGq6q1JntHdn1pYd2J3n3gtz3OvJL/c3T87uERgiUwRAizHszLd7L7o1OtwniMyTUUC24gRLACAwYxgAQAMJmABAAwmYAEADCZgAQAMJmABAAwmYAEADPb/A2jGS5gk/Y5PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 7.5]); # Set dimensions for figure\n",
    "\n",
    "plt.hist(data['Sales'], bins=20, color='#fcba03')\n",
    "plt.title('Histogram of Shampoo Sales');\n",
    "plt.xlabel('Shampoo Sales ($M)');\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10, 7.5]); # Set dimensions for figure\n",
    "\n",
    "sns.distplot(data['Sales'], bins=20, hist=True, kde=True, color='#fcba03');\n",
    "plt.title('Histogram of Shampoo Sales');\n",
    "plt.xlabel('Shampoo Sales ($M)');\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatter Plots "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20, 7.5]); # Set dimensions for figure\n",
    "\n",
    "sns.scatterplot(x=data['Month'], y=data['Sales']);\n",
    "plt.title('Historical Shampoo Sales');\n",
    "plt.ylabel('Shampoo Sales ($M)');\n",
    "plt.xlabel('Month');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset from here: https://www.co2.earth/historical-co2-datasets\n",
    "co2_dataset = pd.read_csv('co2_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20, 7.5]); # Set dimensions for figure\n",
    "\n",
    "# Let's only consider the data from the year 1950\n",
    "X = co2_dataset['year'].values[1950:]\n",
    "y = co2_dataset['data_mean_global'].values[1950:]\n",
    "\n",
    "sns.scatterplot(x=X, y=y);\n",
    "plt.title('Historical Global CO2 Concentration in the Atmosphere');\n",
    "plt.ylabel('CO2 Concentration (ppm)');\n",
    "plt.xlabel('Year');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slope is 1.358871192943465 and the intercept is -2348.0338300545372\n"
     ]
    }
   ],
   "source": [
    "X = co2_dataset['year'].values[1950:].reshape(-1, 1)\n",
    "y = co2_dataset['data_mean_global'].values[1950:].reshape(-1, 1)\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "reg.fit(X, y)\n",
    "\n",
    "print(f\"The slope is {reg.coef_[0][0]} and the intercept is {reg.intercept_[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = co2_dataset['year'].values[1950:]\n",
    "y = co2_dataset['data_mean_global'].values[1950:]\n",
    "\n",
    "predictions = reg.predict(X.reshape(-1, 1))\n",
    "\n",
    "plt.figure(figsize=(20, 8))\n",
    "plt.scatter(X, y,c='black')\n",
    "plt.plot(X, predictions, c='blue', linewidth=2)\n",
    "plt.title('Historical Global CO2 Concentration in the Atmosphere');\n",
    "plt.ylabel('CO2 Concentration (ppm)');\n",
    "plt.xlabel('Year');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = sm.add_constant(co2_dataset['year'].values[1950:])\n",
    "model = sm.OLS(co2_dataset['data_mean_global'].values[1950:], X).fit()\n",
    "residuals = model.resid\n",
    "qq_plot = sm.qqplot(residuals, line='q')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.971\n",
      "Model:                            OLS   Adj. R-squared:                  0.970\n",
      "Method:                 Least Squares   F-statistic:                     2073.\n",
      "Date:                Wed, 29 Apr 2020   Prob (F-statistic):           6.40e-50\n",
      "Time:                        10:20:17   Log-Likelihood:                -189.19\n",
      "No. Observations:                  65   AIC:                             382.4\n",
      "Df Residuals:                      63   BIC:                             386.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -2348.0338     59.161    -39.689      0.000   -2466.259   -2229.809\n",
      "x1             1.3589      0.030     45.526      0.000       1.299       1.419\n",
      "==============================================================================\n",
      "Omnibus:                        7.614   Durbin-Watson:                   0.026\n",
      "Prob(Omnibus):                  0.022   Jarque-Bera (JB):                7.820\n",
      "Skew:                           0.812   Prob(JB):                       0.0200\n",
      "Kurtosis:                       2.497   Cond. No.                     2.09e+05\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.09e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "X = sm.add_constant(co2_dataset['year'].values[1950:])\n",
    "model = sm.OLS(co2_dataset['data_mean_global'].values[1950:], X).fit()\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XAcc+lX8F8JnV/ijNQjf//gHgYFW9Z5U2jzs2T5/k2fR+H/5rdlUOrGmU1/w64Le7vVHOAb5z7N//DeBiVpk+2YjjvcLK9+924GMD2nwKeEmSzd1U7Uu6ZfMz70+EJ70Afw/cCtxC70U4vVv+eOATK9qdR29PhLvoTWHMu+476c2n3dRdju3B8eO66e31cXN3uW2edQ8aP+CP6f0BAngk8E/dz/VF4MlzHt9fpvfv7S0rxvg84DXAa7o2r+3G9WZ6HyQ/dwO8Lwa+5n11h97JVO7q3vtL8667q+skeoH8mBXLNtx40/sDcw/wI3pb1ZfQ+7zm08Ad3fWpXdsl4P0rHvvq7j1+J/CqeY+5h9JLUqOan0KRpIcrA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ16v8BJB6+nbz4fo8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = sm.add_constant(co2_dataset['year'].values[1950:])\n",
    "model = sm.OLS(co2_dataset['data_mean_global'].values[1950:], X).fit()\n",
    "residuals = model.resid\n",
    "\n",
    "plt.hist(residuals);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
